The following account of the prior art relates to one of the areas of application of the present invention, hearing aids.
A considerable body of literature deals with Blind Source Separation (BSS), semi-blind source separation, spatial filtering, noise reduction, beamforming with microphone arrays, or the more overall topic Computational Auditory Scene Analysis (CASA). In general such methods are more or less capable of separating concurrent sound sources either by using different types of cues, such as the cues described in Bregman's book [Bregman, 1990] or used in machine learning approaches [e.g. Roweis, 2001].
Recently binary masks and beamforming where combined in order to extract more concurrent sources than the number of microphones (cf. Pedersen, M. S., Wang, D., Larsen, J., Kjems, U., Overcomplete Blind Source Separation by Combining ICA and Binary Time-Frequency Masking, IEEE International workshop on Machine Learning for Signal Processing, pp. 15-20, 2005). That work was, aimed at being able to separate more than two acoustic sources from two microphones. The general output of such algorithms is either the separated sound source at either source position or at microphone position with none or little information from the other sources. If spatial cues are not available, monaural approaches have been suggested and tested (c.f. e.g. [Jourjine, Richard, and Yilmas, 2000]; [Roweis, 2001]; [Pontoppidan and Dyrholm, 2003]; [Bach and Jordan, 2005]).
Adjustable delays in hearing instruments has been described in EP 1 801 786 A1, where the throughput delay can be adjusted in order to trade off between processing delay and delay artefact. U.S. Pat. No. 7,231,055 B2 teaches a method of removing masking-effects in a hearing aid. The method may include delaying a sound that would otherwise have been masked for the hearing impaired by another sound.